Using UAS swarms for optimal data acquisition in large spatially distributed areas

使用无人机群在大空间分布区域中实现最佳数据采集

• 批准号: 491356-2015
• 负责人: Bonakdarpour, Borzoo
• 金额: $ 1.82万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=586851
• 关键词: Using; UAS; swarms; optimal; data

Unmanned aerial systems (UASs), commonly known as drones, are expected to be at the core of the next-generation technology in many different areas such as emergency-response, search and rescue, transportation, unmanned patrolling, agriculture, area survey and inspection, entertainment, and automated mission control. However, the current application of UASs is limited to relatively small scales due to the currently available battery technology and regulatory requirements. For instance, in a topographical survey application, where a large area of several square kilometers needs to be imaged, using a single drone requires multiple battery replacements and massive ground support throughout the field operation. This may expand the entire operation to several days with significant human support, which may not be economically feasible. This economic infeasibility indeed exists for the industrial partner of the project, Sumac Geomatics Inc. This Engage Project will address the above problem by designing a technique that incorporates a swarm of coordinated UASs for optimal data acquisition in large spatially distributed areas. The technique will combine sophisticated offline and online distributed optimization algorithms to find a mission path for each UAS given a set of spatial, energy, and data acquisition constraints. The combination of these paths should result in an overall optimal mission that achieves the operation objectives. We believe this technique will bridge the gap between area constraints of a typical individual UAS scale of operation and the lower limits of economic feasibility for traditional manned aircraft operations. The results of the project will enable Sumac to accept projects that were not economically feasible, thus, significantly widening the scope and diversity of Sumac's clients and operations. In this context, we also envision several future directions of research partnerships in designing swarms of UASs that have online distributed data processing for traffic monitoring, online inspection, and unmanned patrolling.

无人驾驶航空系统（UAS）通常被称为无人机，预计将成为许多不同领域的下一代技术的核心，例如紧急响应、搜索和救援、运输、无人巡逻、农业、区域调查和检查、娱乐和自动化使命控制。然而，由于目前可用的电池技术和监管要求，UAS的当前应用限于相对较小的规模。例如，在地形测量应用中，需要对几平方公里的大面积进行成像，使用单个无人机需要在整个现场操作中多次更换电池和大量地面支持。这可能会将整个行动延长到几天，需要大量的人力支持，这在经济上可能不可行。这 该项目的工业合作伙伴Sumac Geomatics Inc.确实存在经济上的不可行性。 该Engage项目将通过设计一种技术来解决上述问题，该技术将一群协调的UAS用于在大型空间分布区域中进行最佳数据采集。该技术将结合联合收割机复杂的离线和在线分布式优化算法，找到一个使命路径为每个无人机系统给定的一组空间，能源和数据采集的限制。这些路径的组合应导致实现操作目标的总体最佳使命。我们相信，这种技术将弥合一个典型的个人无人机系统的操作规模和传统的有人驾驶飞机操作的经济可行性的下限之间的区域限制的差距差距。该项目的成果将使Sumac能够接受经济上不可行的项目，从而大大扩大Sumac客户和业务的范围和多样性。在这种情况下，我们还设想了几个未来的研究合作方向，在设计成群的无人机，具有在线分布式数据处理的交通监控，在线检查和无人巡逻。